用语言模型把游戏行为当自定义语言，自动发现玩家群体
Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
- 将游戏事件序列转为文本，用Longformer自监督预训练
- 无需标签就能识别出有意义的玩家分组
- 适合做游戏个性化设计的团队或研究者
本初步研究探索了语言模型（LM）在建模游戏事件序列中的应用，将游戏事件视为一种定制化的自然语言。研究以一款流行移动端游戏为例，将原始事件数据转化为文本序列，并在该数据上对Longformer模型进行预训练。该方法有效捕捉了游戏会话中丰富且细微的互动模式，能够准确识别出具有意义的玩家群体。结果表明，自监督语言模型在不依赖真实标签的情况下，具备提升游戏设计与个性化推荐的潜力。
原文摘要 · Abstract (English)
This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels.
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